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Friday, June 22, 2012

The Model Dilemma

Over the last few months, risk models have come under the spotlight as a potential reason why risk management as an entire institutional function is failing. In the recent JP Morgan CDX tranche 9 blow up, Value at Risk was held accountable in much of the part. The JP Morgan disaster initially put the bank into a negative trajectory of at least USD 2bn and is very much a tail event that might just fall outside a traditional risk modelling technique.

But this is not an isolated case. There have been other claims from many corners of society that risk models are as dangerous as the risk they are attempting to quantify.

In this blog we look at the argument to rebuke the model.Tails of the unexpected ...Andrew Haldane, an executive director at the Bank of England, presented a paper that criticises financial models directly. He is especially concerned about models which are used to quantify catastrophe when these very same models are based on Gaussian principles.

Is economics and finance being fooled by randomness and risk management tools used by financial institutions have in many ways a greater distance still to travel.

VaR doesn't stop a trader constructing a portfolio which delivers a 1% chance of a $1 billion dollar loss, so VaR is blind to that risk.

Attempts to fine-tune risk control may add to the probability of fat-tailed catastrophes. Constraining small bumps in the road may make a system, in particular a social system, more prone to systemic collapse.

This paper is articulate, insightful and very well researched. Importantly, the paper is also logically balanced; why?

In short, Mr Haldane highlights specific flaws in financial models but he also gives insight into the ways in which the industry of banking can evolve the field of risk measurement. In many cases, people that are critical of risk modelling, have a general tendency to reject the models but also have little interest in replacing them with something cognitive as a solution. Regressing back down the tree of evolution is not normally a productive choice unless it is based around rationalisation, optimisation or a tested proof.

Rejecting a model without understanding why at a variable level, is a juvenile reaction and when emotions are running high, many of us fall into this trap. In other cases, I believe some business managers find it easier to blame their mistakes on models such as Value at Risk or VaR as it is better known, than squarely accepting accountability for their actions.

We need to appreciate that risk measurement itself is under a process of metamorphosis and this can be found at an institutional level when a risk framework is being rolled out.

When it comes to modelling directly, a few rules of thumb should be kept in mind:

[1] The risk analyst really needs to understand the limits of their model. Some models are good for normal operation but may not estimate the type II tail events similar to what JP Morgan experienced.

[2] Risk management needs to observe absolute caution when releasing a model or its reports to senior management. Risk analysts need to clearly articulate what the model captures and where oversight or coverage is lacking. They also need to show how the model can be used and what type of business decisions should be drawn from it. As obvious as this statement seems, it is rarely carried out.

[3] Models are very sensitive to the data that is inserted into them. This is also obvious but most bankers and risk departments just aren't there in much of the part.

Me : On this pricing model you are feeding in a set of correlation coefficients?

Analyst : Yes

Me : Where did these coefficients come from?

Analyst : The correl function in Microsoft Excel

Me : What, you used OLS / Linear correlation for this pricing engine?

Analyst : Yes

Me : Surely there is something wrong when we buy a pricing model which is thousands and thousands of dollars and we feed in a weak estimate from Excel?

[4] Finally all models of randomness fall within the random function. You can't predict the future and we must stop believing that we can.

Let's take a look at this model of random bird flocks. If you watch carefully, the centroid's path has a tendency for regime shift. A minor tail switch if you may and that may not be the exception but might just be the balancing rule.

Tamas Vicsek Modelling of Randomness | Swarm Behaviour

In the Vicsek model shown in the video, particles move with a constant speed but switch in random perturbations through a single time increment. The average direction of motion is actually based on the dispersion of the other particles in the cluster.

Vicsek models may be fine for birds but how about Vasicek models for interest rate modelling? If you read Andrew Haldanes BOE paper, you will find that he doesn't like them.Why?

Well they have that same Gaussian curse. They ignore the regime shifts and before that time, business management may have fallen into some kind of sequential complacency, a comfort with the fact that all might just be fine with randomness.

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Author

Martin Davies is a risk framework architect with strong domain knowledge across a diverse set of risk fraternities, a background in banking front-to-back and the ability to articulate business requirements into functional information technology concepts. He is focused on structured products for emerging markets and works with several tier one banks, regulators and brokerages across South East Asia.